Performing occlusion-aware global 3d pose and shape estimation of articulated objects

ABSTRACT

In order to determine accurate three-dimensional (3D) models for objects within a video, the objects are first identified and tracked within the video, and a pose and shape are estimated for these tracked objects. A translation and global orientation are removed from the tracked objects to determine local motion for the objects, and motion infilling is performed to fill in any missing portions for the object within the video. A global trajectory is then determined for the objects within the video, and the infilled motion and global trajectory are then used to determine infilled global motion for the object within the video. This enables the accurate depiction of each object as a 3D pose sequence for that model that accounts for occlusions and global factors within the video.

FIELD OF THE INVENTION

The present invention relates to video analysis, and more particularlyto determining accurate three-dimensional (3D) models from objects in avideo.

BACKGROUND

Recovering fine-grained 3D human meshes from monocular videos isvaluable for understanding human behaviors and interactions, which canbe the cornerstone for numerous applications including virtual oraugmented reality, assistive living, autonomous driving, etc. Many ofthese applications use dynamic cameras to capture human behaviors yetalso require estimating human motions in global coordinates consistentwith their surroundings. For instance, assistive robots and autonomousvehicles need a holistic understanding of human behaviors andinteractions in the world to safely plan their actions even when theyare moving. It is therefore desirable to recover global human meshesfrom monocular videos captured by dynamic cameras, while accounting forscale ambiguity and occlusions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a flowchart of a method for performingocclusion-aware global 3D pose estimation, in accordance with anembodiment.

FIG. 2 illustrates a parallel processing unit, in accordance with anembodiment.

FIG. 3A illustrates a general processing cluster within the parallelprocessing unit of FIG. 2 , in accordance with an embodiment.

FIG. 3B illustrates a memory partition unit of the parallel processingunit of FIG. 2 , in accordance with an embodiment.

FIG. 4A illustrates the streaming multi-processor of FIG. 3A, inaccordance with an embodiment.

FIG. 4B is a conceptual diagram of a processing system implemented usingthe PPU of FIG. 2 , in accordance with an embodiment.

FIG. 4C illustrates an exemplary system in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented.

FIG. 5 illustrates an exemplary occlusion-aware global 3D object poseand shape estimation environment, in accordance with an embodiment.

DETAILED DESCRIPTION

In order to determine accurate three-dimensional (3D) models for objects(such as articulated objects) within a video, the objects are firstidentified and tracked within the video, and a pose and shape areestimated for these tracked objects. A translation and globalorientation are removed from the tracked objects to determine localmotion for the objects, and motion infilling is performed to fill in anymissing portions for the object within the video. A global trajectory isthen determined for the objects within the video, and the infilledmotion and global trajectory are then used to determine infilled globalmotion for the object within the video. This enables the accuratedepiction of each object as a 3D pose sequence for that model thataccounts for occlusions and global factors within the video.

FIG. 1 illustrates a flowchart of a method 100 for performingocclusion-aware global 3D pose estimation, in accordance with anembodiment. Although method 100 is described in the context of aprocessing unit, the method 100 may also be performed by a program,custom circuitry, or by a combination of custom circuitry and a program.For example, the method 100 may be executed by a GPU (graphicsprocessing unit), CPU (central processing unit), or any processingelement. Furthermore, persons of ordinary skill in the art willunderstand that any system that performs method 100 is within the scopeand spirit of embodiments of the present invention.

As shown in operation 102, one or more objects within a video areidentified and tracked. In one embodiment, the video may include amonocular video (e.g., one or more video sources/perspectives of asingle scene). In another embodiment, the video may include a pluralityof video frames from one or more camera positions. In yet anotherembodiment, the video may be produced by one or more cameras within anautomated driving/navigation system.

Additionally, in one embodiment, each object within the video mayinclude any entity within the scene (e.g., a person, vehicle, animal,etc.). In another embodiment, a bounding box may be placed around one ormore objects within each frame of the video. In yet another embodiment,an identifier may be assigned to one or more objects within the video.In still another embodiment, one or more of the objects may includearticulated objects (e.g., objects having two or more sections connectedby at least one flexible joint, etc.).

Further, in one embodiment, objects that are removed from the scene inone or more frames may be re-identified (with a previously-assignedidentifier) and tracked (using a bounding box and/or its content) whenthey reappear within the scene. In another embodiment, the results ofthe identifying and tracking may include a video, where each frame ofthe video includes bounding boxes around each identified object, and anidentifier for each object.

Further still, as shown in operation 104, a pose and shape are estimatedfor one or more tracked objects within the video. In one embodiment, foreach frame within the video, a pose and shape of one or more identifiedand tracked objects within the frame may be determined. In anotherembodiment, within each frame of the video, a pose and shape of one ormore objects within their respective bounding boxes may be determined.

Also, in one embodiment, the pose and shape may be determined for thetracked object for each frame of the video. This may result in adetermination of global motion with respect to the camera for one ormore objects within the video.

In addition, as shown in operation 106, a translation and globalorientation are removed from one or more tracked objects within thevideo. In one embodiment, within each frame of the video, a translationand global orientation may be removed from one or more objects withineach bounding box. In another embodiment, one or more mathematicaloperators may set one or more joints and/or rotation information for oneor more tracked objects within the scene. This may result in adetermination of local motion for one or more objects within theirrespective bounding boxes within the video (as opposed to the previouslydetermined global motion for such objects).

Furthermore, in one embodiment, for each of one or more tracked objects,a three-dimensional (3D) object pose and shape sequence may bedetermined for the tracked object, utilizing the determined local motionfor the tracked object within the video.

Further still, as shown in operation 108, motion infilling is performedfor one or more of the tracked objects within the video. In oneembodiment, one or more missing portions (e.g., missing information) mayexist for one or more tracked objects within one or more frames of thevideo. For example, these missing portions may result from occlusion ofthe object in one or more frames. In another example, these missingportions may result from truncation of the object in one or more frames.In yet another example, these missing portions may result from theobject moving out of the scene in one or more frames.

Also, in one embodiment, a trained neural network architecture maydetermine one or more missing portions for an object within a frame ofthe video. In another embodiment, if one or more missing portions areidentified for an object within a bounding box of a predetermined frameof the video, previous pose and shape data may be identified for theobject within previous bounding boxes of previous frames of the video(e.g., frames occurring before the predetermined frame, etc.).

Additionally, in one embodiment, the previous pose and shape data forthe object may be input into a trained neural network architecture. Inanother embodiment, the trained neural network architecture may predictthe pose and shape data for the object within the predetermined frame ofthe video. In yet another embodiment, the predicted pose and shape datamay be used to fill in the missing portions of the object within thepredetermined frame of the video. In still another embodiment, resultsof performing motion infilling may include infilled local motion for oneor more objects within the video.

Further, as shown in operation 110, a global trajectory is predicted forone or more tracked objects within the video. In one embodiment, foreach object within the video having infilled local motion, the infilledlocal motion may be used to predict a global trajectory for the objectwithin the video. For example, the global trajectory may include atranslation and orientation of the object within a global coordinatesystem. This may recover global orientation/translation for a trackedobject (where such information was previously removed).

Further still, as shown in operation 112, for one or more trackedobjects within the video, the infilled motion for the tracked object iscombined with the global trajectory for the tracked object to determinethe infilled global motion for the tracked object. In one embodiment,the infilled global motion may be determined with respect to a globalcoordinate system. In another embodiment, the infilled motion mayinclude the results of performing motion infilling for the one or moretracked objects within the video.

Also, in one embodiment, one or more camera parameters (e.g., cameramovement (position and orientation) during recording, etc.) may beaccounted for during the determination of the infilled global motion. Inanother embodiment, for each of one or more tracked objects, the 3Dobject pose sequence for that tracked object may be refined utilizingthe infilled global motion for the tracked object. In yet anotherembodiment, the global trajectory for the tracked object may be furtherrefined to make it consistent with image evidence within the video. Forexample, the global trajectory may be refined using an optimizationobjective while also optimizing for extrinsic camera parameters withinthe video.

In this way, a 3D pose sequence may be determined for one or moreobjects within a video, where each 3D pose sequence has no missingportions. This may reduce an amount of processing necessary to implementthese objects within one or more applications (e.g., virtual reality,animation, etc.). Additionally, the 3D representations of the one ormore objects may be determined within a global coordinate system, whichmay also reduce an amount of processing necessary to implement theseobjects within such applications. This may improve a performance ofcomputing hardware performing the applications.

In yet another embodiment, the safety determination may be performedutilizing a parallel processing unit (PPU) such as the PPU 200illustrated in FIG. 2 .

More illustrative information will now be set forth regarding variousoptional architectures and features with which the foregoing frameworkmay be implemented, per the desires of the user. It should be stronglynoted that the following information is set forth for illustrativepurposes and should not be construed as limiting in any manner. Any ofthe following features may be optionally incorporated with or withoutthe exclusion of other features described.

Parallel Processing Architecture

FIG. 2 illustrates a parallel processing unit (PPU) 200, in accordancewith an embodiment. In an embodiment, the PPU 200 is a multi-threadedprocessor that is implemented on one or more integrated circuit devices.The PPU 200 is a latency hiding architecture designed to process manythreads in parallel. A thread (i.e., a thread of execution) is aninstantiation of a set of instructions configured to be executed by thePPU 200. In an embodiment, the PPU 200 is a graphics processing unit(GPU) configured to implement a graphics rendering pipeline forprocessing three-dimensional (3D) graphics data in order to generatetwo-dimensional (2D) image data for display on a display device such asa liquid crystal display (LCD) device. In other embodiments, the PPU 200may be utilized for performing general-purpose computations. While oneexemplary parallel processor is provided herein for illustrativepurposes, it should be strongly noted that such processor is set forthfor illustrative purposes only, and that any processor may be employedto supplement and/or substitute for the same.

One or more PPUs 200 may be configured to accelerate thousands of HighPerformance Computing (HPC), data center, and machine learningapplications. The PPU 200 may be configured to accelerate numerous deeplearning systems and applications including autonomous vehicleplatforms, deep learning, high-accuracy speech, image, and textrecognition systems, intelligent video analytics, molecular simulations,drug discovery, disease diagnosis, weather forecasting, big dataanalytics, astronomy, molecular dynamics simulation, financial modeling,robotics, factory automation, real-time language translation, onlinesearch optimizations, and personalized user recommendations, and thelike.

As shown in FIG. 2 , the PPU 200 includes an Input/Output (I/O) unit205, a front end unit 215, a scheduler unit 220, a work distributionunit 225, a hub 230, a crossbar (Xbar) 270, one or more generalprocessing clusters (GPCs) 250, and one or more partition units 280. ThePPU 200 may be connected to a host processor or other PPUs 200 via oneor more high-speed NVLink 210 interconnect. The PPU 200 may be connectedto a host processor or other peripheral devices via an interconnect 202.The PPU 200 may also be connected to a local memory comprising a numberof memory devices 204. In an embodiment, the local memory may comprise anumber of dynamic random access memory (DRAM) devices. The DRAM devicesmay be configured as a high-bandwidth memory (HBM) subsystem, withmultiple DRAM dies stacked within each device.

The NVLink 210 interconnect enables systems to scale and include one ormore PPUs 200 combined with one or more CPUs, supports cache coherencebetween the PPUs 200 and CPUs, and CPU mastering. Data and/or commandsmay be transmitted by the NVLink 210 through the hub 230 to/from otherunits of the PPU 200 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).The NVLink 210 is described in more detail in conjunction with FIG. 4B.

The I/O unit 205 is configured to transmit and receive communications(i.e., commands, data, etc.) from a host processor (not shown) over theinterconnect 202. The I/O unit 205 may communicate with the hostprocessor directly via the interconnect 202 or through one or moreintermediate devices such as a memory bridge. In an embodiment, the I/Ounit 205 may communicate with one or more other processors, such as oneor more the PPUs 200 via the interconnect 202. In an embodiment, the I/Ounit 205 implements a Peripheral Component Interconnect Express (PCIe)interface for communications over a PCIe bus and the interconnect 202 isa PCIe bus. In alternative embodiments, the I/O unit 205 may implementother types of well-known interfaces for communicating with externaldevices.

The I/O unit 205 decodes packets received via the interconnect 202. Inan embodiment, the packets represent commands configured to cause thePPU 200 to perform various operations. The I/O unit 205 transmits thedecoded commands to various other units of the PPU 200 as the commandsmay specify. For example, some commands may be transmitted to the frontend unit 215. Other commands may be transmitted to the hub 230 or otherunits of the PPU 200 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).In other words, the I/O unit 205 is configured to route communicationsbetween and among the various logical units of the PPU 200.

In an embodiment, a program executed by the host processor encodes acommand stream in a buffer that provides workloads to the PPU 200 forprocessing. A workload may comprise several instructions and data to beprocessed by those instructions. The buffer is a region in a memory thatis accessible (i.e., read/write) by both the host processor and the PPU200. For example, the I/O unit 205 may be configured to access thebuffer in a system memory connected to the interconnect 202 via memoryrequests transmitted over the interconnect 202. In an embodiment, thehost processor writes the command stream to the buffer and thentransmits a pointer to the start of the command stream to the PPU 200.The front end unit 215 receives pointers to one or more command streams.The front end unit 215 manages the one or more streams, reading commandsfrom the streams and forwarding commands to the various units of the PPU200.

The front end unit 215 is coupled to a scheduler unit 220 thatconfigures the various GPCs 250 to process tasks defined by the one ormore streams. The scheduler unit 220 is configured to track stateinformation related to the various tasks managed by the scheduler unit220. The state may indicate which GPC 250 a task is assigned to, whetherthe task is active or inactive, a priority level associated with thetask, and so forth. The scheduler unit 220 manages the execution of aplurality of tasks on the one or more GPCs 250.

The scheduler unit 220 is coupled to a work distribution unit 225 thatis configured to dispatch tasks for execution on the GPCs 250. The workdistribution unit 225 may track a number of scheduled tasks receivedfrom the scheduler unit 220. In an embodiment, the work distributionunit 225 manages a pending task pool and an active task pool for each ofthe GPCs 250. The pending task pool may comprise a number of slots(e.g., 32 slots) that contain tasks assigned to be processed by aparticular GPC 250. The active task pool may comprise a number of slots(e.g., 4 slots) for tasks that are actively being processed by the GPCs250. As a GPC 250 finishes the execution of a task, that task is evictedfrom the active task pool for the GPC 250 and one of the other tasksfrom the pending task pool is selected and scheduled for execution onthe GPC 250. If an active task has been idle on the GPC 250, such aswhile waiting for a data dependency to be resolved, then the active taskmay be evicted from the GPC 250 and returned to the pending task poolwhile another task in the pending task pool is selected and scheduledfor execution on the GPC 250.

The work distribution unit 225 communicates with the one or more GPCs250 via XBar 270. The XBar 270 is an interconnect network that couplesmany of the units of the PPU 200 to other units of the PPU 200. Forexample, the XBar 270 may be configured to couple the work distributionunit 225 to a particular GPC 250. Although not shown explicitly, one ormore other units of the PPU 200 may also be connected to the XBar 270via the hub 230.

The tasks are managed by the scheduler unit 220 and dispatched to a GPC250 by the work distribution unit 225. The GPC 250 is configured toprocess the task and generate results. The results may be consumed byother tasks within the GPC 250, routed to a different GPC 250 via theXBar 270, or stored in the memory 204. The results can be written to thememory 204 via the partition units 280, which implement a memoryinterface for reading and writing data to/from the memory 204. Theresults can be transmitted to another PPU 200 or CPU via the NVLink 210.In an embodiment, the PPU 200 includes a number U of partition units 280that is equal to the number of separate and distinct memory devices 204coupled to the PPU 200. A partition unit 280 will be described in moredetail below in conjunction with FIG. 3B.

In an embodiment, a host processor executes a driver kernel thatimplements an application programming interface (API) that enables oneor more applications executing on the host processor to scheduleoperations for execution on the PPU 200. In an embodiment, multiplecompute applications are simultaneously executed by the PPU 200 and thePPU 200 provides isolation, quality of service (QoS), and independentaddress spaces for the multiple compute applications. An application maygenerate instructions (i.e., API calls) that cause the driver kernel togenerate one or more tasks for execution by the PPU 200. The driverkernel outputs tasks to one or more streams being processed by the PPU200. Each task may comprise one or more groups of related threads,referred to herein as a warp. In an embodiment, a warp comprises 32related threads that may be executed in parallel. Cooperating threadsmay refer to a plurality of threads including instructions to performthe task and that may exchange data through shared memory. Threads andcooperating threads are described in more detail in conjunction withFIG. 4A.

FIG. 3A illustrates a GPC 250 of the PPU 200 of FIG. 2 , in accordancewith an embodiment. As shown in FIG. 3A, each GPC 250 includes a numberof hardware units for processing tasks. In an embodiment, each GPC 250includes a pipeline manager 310, a pre-raster operations unit (PROP)315, a raster engine 325, a work distribution crossbar (WDX) 380, amemory management unit (MMU) 390, and one or more Data ProcessingClusters (DPCs) 320. It will be appreciated that the GPC 250 of FIG. 3Amay include other hardware units in lieu of or in addition to the unitsshown in FIG. 3A.

In an embodiment, the operation of the GPC 250 is controlled by thepipeline manager 310. The pipeline manager 310 manages the configurationof the one or more DPCs 320 for processing tasks allocated to the GPC250. In an embodiment, the pipeline manager 310 may configure at leastone of the one or more DPCs 320 to implement at least a portion of agraphics rendering pipeline. For example, a DPC 320 may be configured toexecute a vertex shader program on the programmable streamingmultiprocessor (SM) 340. The pipeline manager 310 may also be configuredto route packets received from the work distribution unit 225 to theappropriate logical units within the GPC 250. For example, some packetsmay be routed to fixed function hardware units in the PROP 315 and/orraster engine 325 while other packets may be routed to the DPCs 320 forprocessing by the primitive engine 335 or the SM 340. In an embodiment,the pipeline manager 310 may configure at least one of the one or moreDPCs 320 to implement a neural network model and/or a computingpipeline.

The PROP unit 315 is configured to route data generated by the rasterengine 325 and the DPCs 320 to a Raster Operations (ROP) unit, describedin more detail in conjunction with FIG. 3B. The PROP unit 315 may alsobe configured to perform optimizations for color blending, organizepixel data, perform address translations, and the like.

The raster engine 325 includes a number of fixed function hardware unitsconfigured to perform various raster operations. In an embodiment, theraster engine 325 includes a setup engine, a coarse raster engine, aculling engine, a clipping engine, a fine raster engine, and a tilecoalescing engine. The setup engine receives transformed vertices andgenerates plane equations associated with the geometric primitivedefined by the vertices. The plane equations are transmitted to thecoarse raster engine to generate coverage information (e.g., an x, ycoverage mask for a tile) for the primitive. The output of the coarseraster engine is transmitted to the culling engine where fragmentsassociated with the primitive that fail a z-test are culled, andtransmitted to a clipping engine where fragments lying outside a viewingfrustum are clipped. Those fragments that survive clipping and cullingmay be passed to the fine raster engine to generate attributes for thepixel fragments based on the plane equations generated by the setupengine. The output of the raster engine 325 comprises fragments to beprocessed, for example, by a fragment shader implemented within a DPC320.

Each DPC 320 included in the GPC 250 includes an M-Pipe Controller (MPC)330, a primitive engine 335, and one or more SMs 340. The MPC 330controls the operation of the DPC 320, routing packets received from thepipeline manager 310 to the appropriate units in the DPC 320. Forexample, packets associated with a vertex may be routed to the primitiveengine 335, which is configured to fetch vertex attributes associatedwith the vertex from the memory 204. In contrast, packets associatedwith a shader program may be transmitted to the SM 340.

The SM 340 comprises a programmable streaming processor that isconfigured to process tasks represented by a number of threads. Each SM340 is multi-threaded and configured to execute a plurality of threads(e.g., 32 threads) from a particular group of threads concurrently. Inan embodiment, the SM 340 implements a SIMD (Single-Instruction,Multiple-Data) architecture where each thread in a group of threads(i.e., a warp) is configured to process a different set of data based onthe same set of instructions. All threads in the group of threadsexecute the same instructions. In another embodiment, the SM 340implements a SIMT (Single-Instruction, Multiple Thread) architecturewhere each thread in a group of threads is configured to process adifferent set of data based on the same set of instructions, but whereindividual threads in the group of threads are allowed to diverge duringexecution. In an embodiment, a program counter, call stack, andexecution state is maintained for each warp, enabling concurrencybetween warps and serial execution within warps when threads within thewarp diverge. In another embodiment, a program counter, call stack, andexecution state is maintained for each individual thread, enabling equalconcurrency between all threads, within and between warps. Whenexecution state is maintained for each individual thread, threadsexecuting the same instructions may be converged and executed inparallel for maximum efficiency. The SM 340 will be described in moredetail below in conjunction with FIG. 4A.

The MMU 390 provides an interface between the GPC 250 and the partitionunit 280. The MMU 390 may provide translation of virtual addresses intophysical addresses, memory protection, and arbitration of memoryrequests. In an embodiment, the MMU 390 provides one or more translationlookaside buffers (TLBs) for performing translation of virtual addressesinto physical addresses in the memory 204.

FIG. 3B illustrates a memory partition unit 280 of the PPU 200 of FIG. 2, in accordance with an embodiment. As shown in FIG. 3B, the memorypartition unit 280 includes a Raster Operations (ROP) unit 350, a leveltwo (L2) cache 360, and a memory interface 370. The memory interface 370is coupled to the memory 204. Memory interface 370 may implement 32, 64,128, 1024-bit data buses, or the like, for high-speed data transfer. Inan embodiment, the PPU 200 incorporates U memory interfaces 370, onememory interface 370 per pair of partition units 280, where each pair ofpartition units 280 is connected to a corresponding memory device 204.For example, PPU 200 may be connected to up to Y memory devices 204,such as high bandwidth memory stacks or graphics double-data-rate,version 5, synchronous dynamic random access memory, or other types ofpersistent storage.

In an embodiment, the memory interface 370 implements an HBM2 memoryinterface and Y equals half U. In an embodiment, the HBM2 memory stacksare located on the same physical package as the PPU 200, providingsubstantial power and area savings compared with conventional GDDR5SDRAM systems. In an embodiment, each HBM2 stack includes four memorydies and Y equals 4, with HBM2 stack including two 128-bit channels perdie for a total of 8 channels and a data bus width of 1024 bits.

In an embodiment, the memory 204 supports Single-Error CorrectingDouble-Error Detecting (SECDED) Error Correction Code (ECC) to protectdata. ECC provides higher reliability for compute applications that aresensitive to data corruption. Reliability is especially important inlarge-scale cluster computing environments where PPUs 200 process verylarge datasets and/or run applications for extended periods.

In an embodiment, the PPU 200 implements a multi-level memory hierarchy.In an embodiment, the memory partition unit 280 supports a unifiedmemory to provide a single unified virtual address space for CPU and PPU200 memory, enabling data sharing between virtual memory systems. In anembodiment the frequency of accesses by a PPU 200 to memory located onother processors is traced to ensure that memory pages are moved to thephysical memory of the PPU 200 that is accessing the pages morefrequently. In an embodiment, the NVLink 210 supports addresstranslation services allowing the PPU 200 to directly access a CPU'spage tables and providing full access to CPU memory by the PPU 200.

In an embodiment, copy engines transfer data between multiple PPUs 200or between PPUs 200 and CPUs. The copy engines can generate page faultsfor addresses that are not mapped into the page tables. The memorypartition unit 280 can then service the page faults, mapping theaddresses into the page table, after which the copy engine can performthe transfer. In a conventional system, memory is pinned (i.e.,non-pageable) for multiple copy engine operations between multipleprocessors, substantially reducing the available memory. With hardwarepage faulting, addresses can be passed to the copy engines withoutworrying if the memory pages are resident, and the copy process istransparent.

Data from the memory 204 or other system memory may be fetched by thememory partition unit 280 and stored in the L2 cache 360, which islocated on-chip and is shared between the various GPCs 250. As shown,each memory partition unit 280 includes a portion of the L2 cache 360associated with a corresponding memory device 204. Lower level cachesmay then be implemented in various units within the GPCs 250. Forexample, each of the SMs 340 may implement a level one (L1) cache. TheL1 cache is private memory that is dedicated to a particular SM 340.Data from the L2 cache 360 may be fetched and stored in each of the L1caches for processing in the functional units of the SMs 340. The L2cache 360 is coupled to the memory interface 370 and the XBar 270.

The ROP unit 350 performs graphics raster operations related to pixelcolor, such as color compression, pixel blending, and the like. The ROPunit 350 also implements depth testing in conjunction with the rasterengine 325, receiving a depth for a sample location associated with apixel fragment from the culling engine of the raster engine 325. Thedepth is tested against a corresponding depth in a depth buffer for asample location associated with the fragment. If the fragment passes thedepth test for the sample location, then the ROP unit 350 updates thedepth buffer and transmits a result of the depth test to the rasterengine 325. It will be appreciated that the number of partition units280 may be different than the number of GPCs 250 and, therefore, eachROP unit 350 may be coupled to each of the GPCs 250. The ROP unit 350tracks packets received from the different GPCs 250 and determines whichGPC 250 that a result generated by the ROP unit 350 is routed to throughthe Xbar 270. Although the ROP unit 350 is included within the memorypartition unit 280 in FIG. 3B, in other embodiment, the ROP unit 350 maybe outside of the memory partition unit 280. For example, the ROP unit350 may reside in the GPC 250 or another unit.

FIG. 4A illustrates the streaming multi-processor 340 of FIG. 3A, inaccordance with an embodiment. As shown in FIG. 4A, the SM 340 includesan instruction cache 405, one or more scheduler units 410(K), a registerfile 420, one or more processing cores 450, one or more special functionunits (SFUs) 452, one or more load/store units (LSUs) 454, aninterconnect network 480, a shared memory/L1 cache 470.

As described above, the work distribution unit 225 dispatches tasks forexecution on the GPCs 250 of the PPU 200. The tasks are allocated to aparticular DPC 320 within a GPC 250 and, if the task is associated witha shader program, the task may be allocated to an SM 340. The schedulerunit 410(K) receives the tasks from the work distribution unit 225 andmanages instruction scheduling for one or more thread blocks assigned tothe SM 340. The scheduler unit 410(K) schedules thread blocks forexecution as warps of parallel threads, where each thread block isallocated at least one warp. In an embodiment, each warp executes 32threads. The scheduler unit 410(K) may manage a plurality of differentthread blocks, allocating the warps to the different thread blocks andthen dispatching instructions from the plurality of differentcooperative groups to the various functional units (i.e., cores 450,SFUs 452, and LSUs 454) during each clock cycle.

Cooperative Groups is a programming model for organizing groups ofcommunicating threads that allows developers to express the granularityat which threads are communicating, enabling the expression of richer,more efficient parallel decompositions. Cooperative launch APIs supportsynchronization amongst thread blocks for the execution of parallelalgorithms. Conventional programming models provide a single, simpleconstruct for synchronizing cooperating threads: a barrier across allthreads of a thread block (i.e., the syncthreads( ) function). However,programmers would often like to define groups of threads at smaller thanthread block granularities and synchronize within the defined groups toenable greater performance, design flexibility, and software reuse inthe form of collective group-wide function interfaces.

Cooperative Groups enables programmers to define groups of threadsexplicitly at sub-block (i.e., as small as a single thread) andmulti-block granularities, and to perform collective operations such assynchronization on the threads in a cooperative group. The programmingmodel supports clean composition across software boundaries, so thatlibraries and utility functions can synchronize safely within theirlocal context without having to make assumptions about convergence.Cooperative Groups primitives enable new patterns of cooperativeparallelism, including producer-consumer parallelism, opportunisticparallelism, and global synchronization across an entire grid of threadblocks.

A dispatch unit 415 is configured to transmit instructions to one ormore of the functional units. In the embodiment, the scheduler unit410(K) includes two dispatch units 415 that enable two differentinstructions from the same warp to be dispatched during each clockcycle. In alternative embodiments, each scheduler unit 410(K) mayinclude a single dispatch unit 415 or additional dispatch units 415.

Each SM 340 includes a register file 420 that provides a set ofregisters for the functional units of the SM 340. In an embodiment, theregister file 420 is divided between each of the functional units suchthat each functional unit is allocated a dedicated portion of theregister file 420. In another embodiment, the register file 420 isdivided between the different warps being executed by the SM 340. Theregister file 420 provides temporary storage for operands connected tothe data paths of the functional units.

Each SM 340 comprises L processing cores 450. In an embodiment, the SM340 includes a large number (e.g., 128, etc.) of distinct processingcores 450. Each core 450 may include a fully-pipelined,single-precision, double-precision, and/or mixed precision processingunit that includes a floating point arithmetic logic unit and an integerarithmetic logic unit. In an embodiment, the floating point arithmeticlogic units implement the IEEE 754-2008 standard for floating pointarithmetic. In an embodiment, the cores 450 include 64 single-precision(32-bit) floating point cores, 64 integer cores, 32 double-precision(64-bit) floating point cores, and 8 tensor cores.

Tensor cores configured to perform matrix operations, and, in anembodiment, one or more tensor cores are included in the cores 450. Inparticular, the tensor cores are configured to perform deep learningmatrix arithmetic, such as convolution operations for neural networktraining and inferencing. In an embodiment, each tensor core operates ona 4×4 matrix and performs a matrix multiply and accumulate operationD=A×B+C, where A, B, C, and D are 4×4 matrices.

In an embodiment, the matrix multiply inputs A and B are 16-bit floatingpoint matrices, while the accumulation matrices C and D may be 16-bitfloating point or 32-bit floating point matrices. Tensor Cores operateon 16-bit floating point input data with 32-bit floating pointaccumulation. The 16-bit floating point multiply requires 64 operationsand results in a full precision product that is then accumulated using32-bit floating point addition with the other intermediate products fora 4×4×4 matrix multiply. In practice, Tensor Cores are used to performmuch larger two-dimensional or higher dimensional matrix operations,built up from these smaller elements. An API, such as CUDA 9 C++ API,exposes specialized matrix load, matrix multiply and accumulate, andmatrix store operations to efficiently use Tensor Cores from a CUDA-C++program. At the CUDA level, the warp-level interface assumes 16×16 sizematrices spanning all 32 threads of the warp.

Each SM 340 also comprises M SFUs 452 that perform special functions(e.g., attribute evaluation, reciprocal square root, and the like). Inan embodiment, the SFUs 452 may include a tree traversal unit configuredto traverse a hierarchical tree data structure. In an embodiment, theSFUs 452 may include texture unit configured to perform texture mapfiltering operations. In an embodiment, the texture units are configuredto load texture maps (e.g., a 2D array of texels) from the memory 204and sample the texture maps to produce sampled texture values for use inshader programs executed by the SM 340. In an embodiment, the texturemaps are stored in the shared memory/L1 cache 370. The texture unitsimplement texture operations such as filtering operations using mip-maps(i.e., texture maps of varying levels of detail). In an embodiment, eachSM 240 includes two texture units.

Each SM 340 also comprises N LSUs 454 that implement load and storeoperations between the shared memory/L1 cache 470 and the register file420. Each SM 340 includes an interconnect network 480 that connects eachof the functional units to the register file 420 and the LSU 454 to theregister file 420, shared memory/L1 cache 470. In an embodiment, theinterconnect network 480 is a crossbar that can be configured to connectany of the functional units to any of the registers in the register file420 and connect the LSUs 454 to the register file and memory locationsin shared memory/L1 cache 470.

The shared memory/L1 cache 470 is an array of on-chip memory that allowsfor data storage and communication between the SM 340 and the primitiveengine 335 and between threads in the SM 340. In an embodiment, theshared memory/L1 cache 470 comprises 128 KB of storage capacity and isin the path from the SM 340 to the partition unit 280. The sharedmemory/L1 cache 470 can be used to cache reads and writes. One or moreof the shared memory/L1 cache 470, L2 cache 360, and memory 204 arebacking stores.

Combining data cache and shared memory functionality into a singlememory block provides the best overall performance for both types ofmemory accesses. The capacity is usable as a cache by programs that donot use shared memory. For example, if shared memory is configured touse half of the capacity, texture and load/store operations can use theremaining capacity. Integration within the shared memory/L1 cache 470enables the shared memory/L1 cache 470 to function as a high-throughputconduit for streaming data while simultaneously providing high-bandwidthand low-latency access to frequently reused data.

When configured for general purpose parallel computation, a simplerconfiguration can be used compared with graphics processing.Specifically, the fixed function graphics processing units shown in FIG.2 , are bypassed, creating a much simpler programming model. In thegeneral purpose parallel computation configuration, the workdistribution unit 225 assigns and distributes blocks of threads directlyto the DPCs 320. The threads in a block execute the same program, usinga unique thread ID in the calculation to ensure each thread generatesunique results, using the SM 340 to execute the program and performcalculations, shared memory/L1 cache 470 to communicate between threads,and the LSU 454 to read and write global memory through the sharedmemory/L1 cache 470 and the memory partition unit 280. When configuredfor general purpose parallel computation, the SM 340 can also writecommands that the scheduler unit 220 can use to launch new work on theDPCs 320.

The PPU 200 may be included in a desktop computer, a laptop computer, atablet computer, servers, supercomputers, a smart-phone (e.g., awireless, hand-held device), personal digital assistant (PDA), a digitalcamera, a vehicle, a head mounted display, a hand-held electronicdevice, and the like. In an embodiment, the PPU 200 is embodied on asingle semiconductor substrate. In another embodiment, the PPU 200 isincluded in a system-on-a-chip (SoC) along with one or more otherdevices such as additional PPUs 200, the memory 204, a reducedinstruction set computer (RISC) CPU, a memory management unit (MMU), adigital-to-analog converter (DAC), and the like.

In an embodiment, the PPU 200 may be included on a graphics card thatincludes one or more memory devices 204. The graphics card may beconfigured to interface with a PCIe slot on a motherboard of a desktopcomputer. In yet another embodiment, the PPU 200 may be an integratedgraphics processing unit (iGPU) or parallel processor included in thechipset of the motherboard.

Exemplary Computing System

Systems with multiple GPUs and CPUs are used in a variety of industriesas developers expose and leverage more parallelism in applications suchas artificial intelligence computing. High-performance GPU-acceleratedsystems with tens to many thousands of compute nodes are deployed indata centers, research facilities, and supercomputers to solve everlarger problems. As the number of processing devices within thehigh-performance systems increases, the communication and data transfermechanisms need to scale to support the increased bandwidth.

FIG. 4B is a conceptual diagram of a processing system 400 implementedusing the PPU 200 of FIG. 2 , in accordance with an embodiment. Theexemplary system 465 may be configured to implement the method 100 shownin FIG. 1 . The processing system 400 includes a CPU 430, switch 410,and multiple PPUs 200 each and respective memories 204. The NVLink 210provides high-speed communication links between each of the PPUs 200.Although a particular number of NVLink 210 and interconnect 202connections are illustrated in FIG. 4B, the number of connections toeach PPU 200 and the CPU 430 may vary. The switch 410 interfaces betweenthe interconnect 202 and the CPU 430. The PPUs 200, memories 204, andNVLinks 210 may be situated on a single semiconductor platform to form aparallel processing module 425. In an embodiment, the switch 410supports two or more protocols to interface between various differentconnections and/or links.

In another embodiment (not shown), the NVLink 210 provides one or morehigh-speed communication links between each of the PPUs 200 and the CPU430 and the switch 410 interfaces between the interconnect 202 and eachof the PPUs 200. The PPUs 200, memories 204, and interconnect 202 may besituated on a single semiconductor platform to form a parallelprocessing module 425. In yet another embodiment (not shown), theinterconnect 202 provides one or more communication links between eachof the PPUs 200 and the CPU 430 and the switch 410 interfaces betweeneach of the PPUs 200 using the NVLink 210 to provide one or morehigh-speed communication links between the PPUs 200. In anotherembodiment (not shown), the NVLink 210 provides one or more high-speedcommunication links between the PPUs 200 and the CPU 430 through theswitch 410. In yet another embodiment (not shown), the interconnect 202provides one or more communication links between each of the PPUs 200directly. One or more of the NVLink 210 high-speed communication linksmay be implemented as a physical NVLink interconnect or either anon-chip or on-die interconnect using the same protocol as the NVLink210.

In the context of the present description, a single semiconductorplatform may refer to a sole unitary semiconductor-based integratedcircuit fabricated on a die or chip. It should be noted that the termsingle semiconductor platform may also refer to multi-chip modules withincreased connectivity which simulate on-chip operation and makesubstantial improvements over utilizing a conventional busimplementation. Of course, the various circuits or devices may also besituated separately or in various combinations of semiconductorplatforms per the desires of the user. Alternately, the parallelprocessing module 425 may be implemented as a circuit board substrateand each of the PPUs 200 and/or memories 204 may be packaged devices. Inan embodiment, the CPU 430, switch 410, and the parallel processingmodule 425 are situated on a single semiconductor platform.

In an embodiment, the signaling rate of each NVLink 210 is 20 to 25Gigabits/second and each PPU 200 includes six NVLink 210 interfaces (asshown in FIG. 4B, five NVLink 210 interfaces are included for each PPU200). Each NVLink 210 provides a data transfer rate of 25Gigabytes/second in each direction, with six links providing 300Gigabytes/second. The NVLinks 210 can be used exclusively for PPU-to-PPUcommunication as shown in FIG. 4B, or some combination of PPU-to-PPU andPPU-to-CPU, when the CPU 430 also includes one or more NVLink 210interfaces.

In an embodiment, the NVLink 210 allows direct load/store/atomic accessfrom the CPU 430 to each PPU's 200 memory 204. In an embodiment, theNVLink 210 supports coherency operations, allowing data read from thememories 204 to be stored in the cache hierarchy of the CPU 430,reducing cache access latency for the CPU 430. In an embodiment, theNVLink 210 includes support for Address Translation Services (ATS),allowing the PPU 200 to directly access page tables within the CPU 430.One or more of the NVLinks 210 may also be configured to operate in alow-power mode.

FIG. 4C illustrates an exemplary system 465 in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented. The exemplary system 465 may be configured toimplement the method 100 shown in FIG. 1 .

As shown, a system 465 is provided including at least one centralprocessing unit 430 that is connected to a communication bus 475. Thecommunication bus 475 may be implemented using any suitable protocol,such as PCI (Peripheral Component Interconnect), PCI-Express, AGP(Accelerated Graphics Port), HyperTransport, or any other bus orpoint-to-point communication protocol(s). The system 465 also includes amain memory 440. Control logic (software) and data are stored in themain memory 440 which may take the form of random access memory (RAM).

The system 465 also includes input devices 460, the parallel processingsystem 425, and display devices 445, i.e. a conventional CRT (cathoderay tube), LCD (liquid crystal display), LED (light emitting diode),plasma display or the like. User input may be received from the inputdevices 460, e.g., keyboard, mouse, touchpad, microphone, and the like.Each of the foregoing modules and/or devices may even be situated on asingle semiconductor platform to form the system 465. Alternately, thevarious modules may also be situated separately or in variouscombinations of semiconductor platforms per the desires of the user.

Further, the system 465 may be coupled to a network (e.g., atelecommunications network, local area network (LAN), wireless network,wide area network (WAN) such as the Internet, peer-to-peer network,cable network, or the like) through a network interface 435 forcommunication purposes.

The system 465 may also include a secondary storage (not shown). Thesecondary storage includes, for example, a hard disk drive and/or aremovable storage drive, representing a floppy disk drive, a magnetictape drive, a compact disk drive, digital versatile disk (DVD) drive,recording device, universal serial bus (USB) flash memory. The removablestorage drive reads from and/or writes to a removable storage unit in awell-known manner.

Computer programs, or computer control logic algorithms, may be storedin the main memory 440 and/or the secondary storage. Such computerprograms, when executed, enable the system 465 to perform variousfunctions. The memory 440, the storage, and/or any other storage arepossible examples of computer-readable media.

The architecture and/or functionality of the various previous figuresmay be implemented in the context of a general computer system, acircuit board system, a game console system dedicated for entertainmentpurposes, an application-specific system, and/or any other desiredsystem. For example, the system 465 may take the form of a desktopcomputer, a laptop computer, a tablet computer, servers, supercomputers,a smart-phone (e.g., a wireless, hand-held device), personal digitalassistant (PDA), a digital camera, a vehicle, a head mounted display, ahand-held electronic device, a mobile phone device, a television,workstation, game consoles, embedded system, and/or any other type oflogic.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

Machine Learning

Deep neural networks (DNNs) developed on processors, such as the PPU 200have been used for diverse use cases, from self-driving cars to fasterdrug development, from automatic image captioning in online imagedatabases to smart real-time language translation in video chatapplications. Deep learning is a technique that models the neurallearning process of the human brain, continually learning, continuallygetting smarter, and delivering more accurate results more quickly overtime. A child is initially taught by an adult to correctly identify andclassify various shapes, eventually being able to identify shapeswithout any coaching. Similarly, a deep learning or neural learningsystem needs to be trained in object recognition and classification forit get smarter and more efficient at identifying basic objects, occludedobjects, etc., while also assigning context to objects.

At the simplest level, neurons in the human brain look at various inputsthat are received, importance levels are assigned to each of theseinputs, and output is passed on to other neurons to act upon. Anartificial neuron or perceptron is the most basic model of a neuralnetwork. In one example, a perceptron may receive one or more inputsthat represent various features of an object that the perceptron isbeing trained to recognize and classify, and each of these features isassigned a certain weight based on the importance of that feature indefining the shape of an object.

A deep neural network (DNN) model includes multiple layers of manyconnected perceptrons (e.g., nodes) that can be trained with enormousamounts of input data to quickly solve complex problems with highaccuracy. In one example, a first layer of the DLL model breaks down aninput image of an automobile into various sections and looks for basicpatterns such as lines and angles. The second layer assembles the linesto look for higher level patterns such as wheels, windshields, andmirrors. The next layer identifies the type of vehicle, and the finalfew layers generate a label for the input image, identifying the modelof a specific automobile brand.

Once the DNN is trained, the DNN can be deployed and used to identifyand classify objects or patterns in a process known as inference.Examples of inference (the process through which a DNN extracts usefulinformation from a given input) include identifying handwritten numberson checks deposited into ATM machines, identifying images of friends inphotos, delivering movie recommendations to over fifty million users,identifying and classifying different types of automobiles, pedestrians,and road hazards in driverless cars, or translating human speech inreal-time.

During training, data flows through the DNN in a forward propagationphase until a prediction is produced that indicates a labelcorresponding to the input. If the neural network does not correctlylabel the input, then errors between the correct label and the predictedlabel are analyzed, and the weights are adjusted for each feature duringa backward propagation phase until the DNN correctly labels the inputand other inputs in a training dataset. Training complex neural networksrequires massive amounts of parallel computing performance, includingfloating-point multiplications and additions that are supported by thePPU 200. Inferencing is less compute-intensive than training, being alatency-sensitive process where a trained neural network is applied tonew inputs it has not seen before to classify images, translate speech,and generally infer new information.

Neural networks rely heavily on matrix math operations, and complexmulti-layered networks require tremendous amounts of floating-pointperformance and bandwidth for both efficiency and speed. With thousandsof processing cores, optimized for matrix math operations, anddelivering tens to hundreds of TFLOPS of performance, the PPU 200 is acomputing platform capable of delivering performance required for deepneural network-based artificial intelligence and machine learningapplications.

Exemplary Environment

FIG. 5 illustrates an exemplary occlusion-aware global 3D object poseand shape estimation environment 500, according to one exemplaryembodiment. As shown, a video 502 is input into an object tracker andre-identifier 504, which places a bounding box around each object to betracked within each frame of the video, assigns an identifier to eachtracked object within the video, and re-identifies objects that areremoved from one or more frames in the video and that re-appear in laterframes of the video.

Additionally, results created by the object tracker and re-identifier504 (e.g., video with bounding boxes around each identified object, andan identifier for each object) may be input into a pose and shapeestimator 506. In one embodiment, the pose and shape estimator 506 maydetermine a pose and shape for each identified and tracked object withineach frame of the video. In one embodiment, the pose and shape may berepresented as a triangulated mesh.

Further, in one embodiment, within the pose and shape estimator 506, atranslation and global orientation may be removed from each trackedobject within the video. For example, within each frame of the video, atranslation and global orientation may be removed from one or moreobjects within each bounding box. This may result in a determination oflocal motion for one or more objects within their respective boundingboxes within the video (as opposed to the previously determined globalmotion for such objects).

Further still, results created by the pose and shape estimator 506(e.g., a determination of local motion for each object within itsrespective bounding box within the video) may be input into a motioninfiller 508. In one embodiment, the motion infiller 508 may identifyand fill in all missing portions for all tracked objects within thevideo, utilizing previous pose and shape data for the tracked objects asinput into a trained neural network.

Also, results created by the motion infiller 508 (e.g., the infilledlocal motion for all objects within their respective bounding boxeswithin the video) may be input into a trajectory predictor 510. In oneembodiment, for each object, the motion infiller 508 may use theinfilled local motion for that object to predict a global trajectory(e.g., a 3D position and orientation with respect to a camera at eachvideo frame) for the object within the video.

In addition, results created by the trajectory predictor 510 (e.g., aglobal trajectory for each object within their respective bounding boxwithin the video) may be input into a global optimizer 512 along withthe infilled local motion for all objects within their respectivebounding boxes within the video. Utilizing these inputs, the globaloptimizer 512 may determine the infilled global motion for each objectwith respect to a global coordinate system, and may determine athree-dimensional (3D) pose sequence (e.g., a sequence of 3D meshes) 514for each object utilizing the infilled global motion for such objectwithin the video 502.

In this way, the occlusion-aware global 3D object pose estimationenvironment 500 may determine a 3D model and pose sequence for eachobject within a video, where each 3D model and pose sequence isrepresented within a global coordinate system and has no missingportions.

Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras

In one embodiment, an approach is provided for 3D global human meshrecovery from monocular videos recorded with dynamic cameras. Thisapproach is robust to severe and long-term occlusions and tracks humanbodies even when they go outside the camera's field of view. To achievethis, a deep generative motion infiller is provided whichautoregressively infills the body motions of occluded humans based onvisible motions. Additionally, in contrast to prior work, this approachreconstructs human meshes in consistent global coordinates even withdynamic cameras. Since the joint reconstruction of human motions andcamera poses is underconstrained, a global trajectory predictor isprovided that generates global human trajectories based on local bodymovements. Using the predicted trajectories as anchors, we present aglobal optimization framework that refines the predicted trajectoriesand optimizes the camera poses to match the video evidence such as 2Dkeypoints.

This task is highly challenging for two main reasons. First, dynamiccameras make it difficult to estimate human motions in consistent globalcoordinates. Existing human mesh recovery methods estimate human meshesin the camera coordinates or even in the root-relative coordinates.Hence, they can only recover global human meshes from dynamic cameras byusing SLAM to estimate camera poses. However, SLAM can often fail forin-the-wild videos due to moving and dynamic objects. It also has theproblem of scale ambiguity, which often leads to camera poses that areinconsistent with the human motions. Second, videos captured by dynamiccameras often contain severe and long-term occlusions of humans, whichcan be caused by missed detection, complete obstruction by objects andother people, or the person going outside the camera's field of view(FoV). These occlusions pose serious challenges to standard human meshrecovery methods, which rely on detections or visible parts to estimatehuman meshes. Existing methods can only address partial occlusions of aperson and fail to handle severe occlusions when the person iscompletely invisible for an extended period of time.

To tackle the above challenges, Global Occlusion-Aware Human MeshRecovery (GLAMR) is provided, which can handle severe occlusions andestimate human meshes in consistent global coordinates—even for videosrecorded with dynamic cameras. First, an estimation is made of the shapeand pose sequences (motions) of visible people in the cameracoordinates. Multiobject tracking and re-identification provideocclusion information, and the motion of occluded frames is notestimated. To tackle potentially severe occlusions, a deep generativemotion infiller is used that autoregressively infills the local bodymotions of occluded people based on visible motions. The motion infillerleverages human dynamics learned from a motion database. Next, to obtainglobal motions, a global trajectory predictor may be used that cangenerate global human trajectories based on local body motions. It ismotivated by the observation that the global root trajectory of a personis highly correlated with the local body movements. Finally, using thepredicted trajectories as anchors to constrain the solution space, aglobal optimization framework may jointly optimize the global motionsand camera poses to match the video evidence such as 2D keypoints.

In this way, long-term occlusions are addressed and global 3D human poseand shape are estimated from videos captured by dynamic cameras.Additionally, a generative Transformer-based motion infillerautoregressively infills long-term missing motions. Further, a method isprovided to generate global human trajectories from local body motionsand use the generated trajectories as anchors to constrain global motionand camera optimization.

The input to the framework is a video I=(I₁, . . . , I_(T)) with Tframes, which is captured by a dynamic camera, i.e., the camera posescan change every frame. Our goal is to estimate the global motion (posesequence) {Q^(i)}_(i=1) ^(N) of the N people in the video in aconsistent global coordinate system. The global motion Q^(i)=(T^(i),R^(i), Θ^(i), B^(i)) for person i consists of the root translationsT^(i)=(τ_(s) _(i) ^(i), . . . , τ_(e) _(i) ^(i)), root rotations R^(i)=(

_(s) _(i) ^(i), . . . ,

_(e) _(i) ^(i)), as well as the body motion Θ^(i)=(θ_(s) _(i) ^(i), . .. , θ_(e) _(i) ^(i)) and shapes B^(i)=(β_(s) _(i) ^(i), . . . , β_(e)_(i) ^(i)), where the motion spans from the first frame s_(i) to thelast frame e_(i), when the person i is relevant in the video. Inparticular, each body pose θ_(t) ^(i)∈

^(23×3) and shape β_(t) ^(i)∈

¹⁰ corresponds to the pose parameters (joint rotations excluding theroot rotation) and shape parameters of the SMPL model. Using the roottranslation τ∈

³ and (axis-angle) rotation

∈

³, SMPL represents a human body mesh with a linear function S(τ, γ, θ,β) that maps a global pose q=(τ, γ, θ, β) to an articulated triangulatedmesh Φ∈

^(K×3) with K=6980 vertices. The global mesh sequence for each personmay be recovered from their global motion Q^(i) via SMPL.

An exemplary framework consists of four stages. In Stage I, multi-objecttracking (MOT) and re-identification algorithms are used to obtain thebounding box sequence of each person, which is input to a human meshrecovery method (e.g., KAMA or SPEC) to extract the motion {tilde over(Q)}^(i) of each person (including translation) in the cameracoordinates. The motion {tilde over (Q)}^(i) may be incomplete due tovarious occlusions (e.g., obstruction, missed detection, going outsideFoV), where bounding boxes from MOT are missing for some frames.

In Stage II, a generative motion infiller may address the occlusions inthe estimated body motion {tilde over (Θ)}^(i) and produceocclusion-free body motion {circumflex over (Θ)}^(i). In Stage III, aglobal trajectory predictor uses the infilled body motion {circumflexover (Θ)}^(i) to generate the global trajectory (root translations androtations) of each person and obtain their global motion {circumflexover (Q)}^(i). In Stage IV, the global trajectories of all people andthe camera parameters are jointly optimized to produce global motions{hacek over (Q)}^(i) consistent with the video evidence.

Generative Motion Infiller

The task of the generative motion infiller M is to infill the occludedbody motion {tilde over (Θ)}^(i) of each person to produceocclusion-free body motion {circumflex over (Θ)}^(i). Here, the motioninfiller M is not used to infill other components in the estimatedmotion {circumflex over (Q)}^(i), i.e., root trajectory ({tilde over(T)}^(i), {tilde over (R)}^(i)) and shapes {tilde over (B)}^(i). This isbecause it is difficult to infill the root trajectory ({tilde over(T)}^(i), {tilde over (R)}^(i)) using learned human dynamics, since itresides in the camera coordinates rather than a consistent coordinatesystem due to the dynamic camera. In one embodiment, the proposed globaltrajectory predictor may be used to generate occlusion-free globaltrajectory ({circumflex over (T)}^(i), {circumflex over (R)}^(i)) fromthe infilled body motion {circumflex over (Θ)}^(i). The trajectory({tilde over (T)}^(i), {tilde over (R)}^(i)) from the pose estimator isnot discarded and will be used in the global optimization. For theshapes, linear interpolation may be used to produce occlusion-freeshapes {circumflex over (B)}^(i) since a person's shape should stayclose to a constant throughout the video.

Given a general occluded human body motion {tilde over (Θ)}=({tilde over(θ)}_(i), . . . , {tilde over (θ)}_(h)) of h frames and its visibilitymask V=(V₁, . . . , V_(h)) as input, the motion infiller M outputs acomplete occlusion-free motion {circumflex over (Θ)}=({circumflex over(θ)}₁, . . . , {circumflex over (θ)}_(h)). The visibility mask V encodesthe visibility of the occluded motion {tilde over (Θ)}, where V_(t)=1 ifthe body pose {tilde over (θ)}_(t) is visible in frame t and V_(t)=0otherwise. Since the human pose for occluded frames can be highlyuncertain and stochastic, the motion infiller M may be formulated usingthe conditional variational autoencoder (CVAE):

{circumflex over (Θ)}=

({tilde over (Θ)},V,z),  (1)

where the motion infiller M corresponds to the CVAE decoder and z is aGaussian latent code. Different occlusion-free motions {circumflex over(Θ)} may be obtained by varying z.

Autoregressive Motion Infilling

To ensure that the motion infiller M can handle much longer test motionsthan the training motions, an autoregressive motion infilling processmay be used at test time. A sliding window of h frames may be used, itis assumed the first h_(c) frames of motion are already occlusion-freeor infilled and serve as context, and the last h₁ frames are used aslook-ahead. The look-ahead is useful to the motion infiller since it maycontain visible poses that can guide the ending motion and avoidgenerating discontinuous motions. Excluding the context and look-aheadframes, only the middle h_(o)=h−h_(c)−h₁ frames of motion are infilled.The motion is iteratively infilled using the sliding window and thewindow is advanced by h_(o) frames every step.

Motion Infiller Network

The overall network design of the CVAE-based motion infiller employs aTransformer-based seq2seq architecture, which consists of three parts:(1) a context network that uses a Transformer encoder to encode thevisible poses from the occluded motion {tilde over (Θ)} into a contextsequence, which serves as the condition for other networks; (2) adecoder network that uses the latent code z and context sequence togenerate occlusion-free motion {circumflex over (Θ)} via a Transformerdecoder and a multilayer perceptron (MLP); and (3) prior and posteriornetworks that generate the prior and posterior distributions for thelatent code z.

In the transformer-based networks, a time-based encoding replaces theposition in the original positional encoding with the time index. Unlikeprior CNN-based methods the Transformer-based motion infiller does notrequire padding missing frames, but instead restricts its attention tovisible frames to achieve effective temporal modeling.

Training

The motion infiller M is trained using a large motion capture dataset(e.g., AMASS, etc.). To synthesize occluded motions {tilde over (Θ)},for any GT training motion {tilde over (Θ)}^(l) of h frames, H_(occ)consecutive frames of motion are randomly occluded where H_(occ) isuniformly sampled from [H_(lb), H_(ub)]. Note that the first h_(c)frames are not occluded and are reserved as context. A standard CVAEobjective may be used to train the motion infiller M:

$\begin{matrix}{{L_{\mathcal{M}} = {{\sum\limits_{t = 1}^{h}{{{\overset{\sim}{\theta}}_{t} - {\overset{\sim}{\theta}}_{t}^{\prime}}}_{2}^{2}} + L_{KL}^{\mathcal{z}}}},} & (2)\end{matrix}$

where

is the KL divergence between the prior and posterior distributions ofthe CVAE latent code z.

Global Trajectory Predictor

After occlusion-free body motion {circumflex over (Θ)}^(i) is obtainedfor each person using the motion infiller, a key issue still remains:the estimated trajectory ({tilde over (T)}^(i), {tilde over (R)}^(i)) ofthe person is still occluded and not in a consistent global coordinatesystem. To tackle this problem, a global trajectory predictor T may belearned that generates a person's occlusion-free global trajectory({circumflex over (T)}^(i), {circumflex over (R)}^(i)) from the localbody motion {circumflex over (Θ)}^(i).

Given a general occlusion-free body motion Θ=(θ₁, . . . , θ_(m)) asinput, the trajectory predictor T outputs its corresponding globaltrajectory (T, R) including the root translations T=(τ₁, . . . , τ_(m))and rotations R=(γ₁, . . . , γ_(m)). To address any potential ambiguityin the global trajectory, the global trajectory predictor is alsoformulated using the CVAE:

Ψ=

(Θ,v),  (3)

(T,R)=EgoToGlobal(Ψ),  (4)

where the global trajectory predictor T corresponds to the CVAE decoderand v is the latent code for the CVAE. In equation (3), the immediateoutput of the global trajectory predictor T is an egocentric trajectoryΨ=(ψ₁, . . . , ψ_(m)), which by design can be converted to a globaltrajectory (T, R) using a conversion function EgoToGlobal.

Egocentric Trajectory Representation

The egocentric trajectory Ψ is just an alternative representation of theglobal trajectory (T, R). It converts the global trajectory intorelative local differences and represents rotations and translations inthe heading coordinates (y-axis aligned with the heading, i.e., theperson's facing direction). In this way, the egocentric trajectoryrepresentation is invariant of the absolute xy translation and heading.It is more suitable for the prediction of long trajectories, since thenetwork only needs to output the local trajectory change of every frameinstead of the potentially large global trajectory offset.

The conversion from the global trajectory to the egocentric trajectoryis given by another function: Ψ=GlobalToEgo(T, R), which is the inverseof the function EgoToGlobal. In particular, the egocentric trajectoryψ_(t)=(δx_(t), δy_(t), z_(t), δϕ_(t), η_(t)) at time t is computed as:

(δx _(t) ,δy _(t))=ToHeading(τ_(x) ^(xy)−τ_(t−1) ^(xy)),  (5)

z _(t)=τ_(t) ^(z), δϕ_(t)=

_(t) ^(ϕ)−

_(t−1) ^(ϕ),  (6)

η_(t)=ToHeading(

_(t)),  (7)

where τ_(t) ^(xy) is the xy component of the translation τ_(t), τ_(t)^(z) is the z component (height) of τ_(t),

_(t) ^(ϕ) is the heading angle of the rotation γ_(t), and ToHeading is afunction that converts translations or rotations to the headingcoordinates defined by the heading

_(t) ^(ϕ). As an exception, (δx₀, δy₀) and δϕ₀ are used to store theinitial xy translation τ₀ ^(xy) and heading τ₀ ^(ϕ).

These initial values are set to the GT during training and arbitraryvalues during inference (as the trajectory can start from any positionand heading). The inverse process of equations (5)-(7) defines theinverse conversion EgoToGlobal used in equation (4), which accumulatesthe egocentric trajectory of each frame to obtain the global trajectory.To correct potential drifts in the trajectory, the global trajectory ofeach person may be optimized to match the video evidence, which alsosolves the trajectory's initial position and heading (δx₀, δy₀, δϕ₀).

Network and Training

The trajectory predictor adopts a similar network design as the motioninfiller with one main difference: LSTMs may be used for temporalmodeling instead of Transformers since the output of each frame is thelocal trajectory change in the egocentric trajectory representation,which mainly depends on the body motion of nearby frames and does notrequire long-range temporal modeling. The egocentric trajectory and useof LSTMs instead of Transformers are beneficial for accurate trajectoryprediction. A CVAE objective may be used to train the trajectorypredictor T:

$\begin{matrix}{{L_{\mathcal{T}} = {{\sum\limits_{t = 1}^{m}\left( {{{\tau_{t} - \tau_{t}^{\prime}}}_{2}^{2} + {{\gamma_{t} \ominus \gamma_{t}^{\prime}}}_{a}^{2}} \right)} + L_{KL}^{v}}},} & (8)\end{matrix}$

where τ′_(t) and

′_(t) denote the GT translation and rotation, ⊖ computes the relativerotation, ∥⋅∥_(a) computes the rotation angle, and

is the KL divergence between the prior and posterior distributions ofthe CVAE latent code v. AMASS may be used to train the trajectorypredictor T.

Global Optimization

After using the generative motion infiller and global trajectorypredictor, an occlusion-free global motion {circumflex over(Q)}^(i)=({circumflex over (T)}^(i), {circumflex over (R)}^(i),{circumflex over (Θ)}^(i), {circumflex over (B)}^(i)) is obtained foreach person in the video. However, the global trajectory predictorgenerates trajectories for each person independently, which may not beconsistent with the video evidence. To address this, a globaloptimization process jointly optimizes the global trajectories of allpeople and the extrinsic camera parameters to match the video evidencesuch as 2D keypoints. The final output of the global optimization andthe framework is {hacek over (Q)}^(i)=(Ť^(i), Ř^(i), {hacek over(Θ)}^(i), {hacek over (B)}^(i)), where ({hacek over (Θ)}^(i), {hacekover (B)}^(i))=({circumflex over (Θ)}^(i), {circumflex over (B)}^(i)),i.e., the occlusion-free body motion and shapes are directly used fromthe previous stages.

Optimization Variables

The first set of variables to be optimized is the egocentricrepresentation {{hacek over (Ψ)}^(i)}_(i=1) ^(N) of the globaltrajectories {(Ť^(i), Ř^(i))}_(i=1) ^(N). The egocentric representationis used since it allows corrections of the translation and heading atone frame to propagate to all future frames. The second set ofoptimization variables is the extrinsic camera parameters C=(C₁, . . . ,C_(T)) where C_(t.) ∈

^(4×4) is the camera extrinsic matrix at frame t of the video.

Energy Function

The energy function we aim to minimize is defined as

E({{hacek over (Ψ)}^(i)}_(i=1) ^(N) ,C)=λ_(2D) E _(2D)+λ_(traj) E_(traj)+λ_(reg) E _(reg)+λ_(cam) E _(cam)+λ_(pen) E _(pen),  (9)

where five energy terms are used with their corresponding coefficientsλ_(2D), λ_(traj), λ_(reg), λ_(cam), and λ_(pen). The first term E_(2D)measures the error between the 2D projection {hacek over (x)}_(t) ^(i)of the optimized 3D keypoints {hacek over (X)}_(t) ^(i)∈

^(J×3) and the estimated 2D keypoints {tilde over (x)}_(t) ^(i) from akeypoint detector:

$\begin{matrix}{{E_{2D} = {\frac{1}{NTJ}{\sum\limits_{i = 1}^{N}{\sum\limits_{t = 1}^{T}{V_{t}^{i}{{{\check{x}}_{t}^{i} - {\overset{\sim}{x}}_{t}^{i}}}_{F}^{2}}}}}},} & (10)\end{matrix}$ $\begin{matrix}{{{\check{x}}_{t}^{i} = {\Pi\left( {{\check{X}}_{t}^{i},C_{t},K} \right)}},{{\check{X}}_{t}^{i} = {\mathcal{J}\left( {{\check{\tau}}_{t}^{i},{\check{\gamma}}_{t}^{i},{\check{\theta}}_{t}^{i},{\check{\beta}}_{t}^{i}} \right)}},} & (11)\end{matrix}$

where V_(t) ^(i) is person i's visibility at frame t, Π is the cameraprojection with extrinsics C_(t) and approximated intrinsics K, and{hacek over (X)}_(t) ^(i) is computed using the SMPL's joint regressor

from the optimized global pose {hacek over (q)}_(t) ^(i)=({hacek over(τ)}_(t) ^(i),

, {hacek over (θ)}_(t) ^(i), {hacek over (β)}_(t) ^(i))∈{hacek over(Q)}^(i). The second term E_(traj) measures the difference between theoptimized global trajectory (Ť^(i), Ř^(i)) viewed in the cameracoordinates and the trajectory ({tilde over (T)}^(i), {tilde over(R)}^(i)) output by the pose estimator (e.g., KAMA) in Stage I:

$\begin{matrix}{{E_{traj} = {\frac{1}{NT}{\sum\limits_{i = 1}^{N}{\sum\limits_{t = 1}^{T}{V_{t}^{i}\left( {{{{\Gamma\left( {{\check{\gamma}}_{t}^{i},C_{t}} \right)} \ominus {\overset{\sim}{\gamma}}_{t}^{i}}}_{a}^{2} + {w_{t}{{{\Gamma\left( {{\check{\tau}}_{t}^{i},C_{t}} \right)} - {\overset{\sim}{\tau}}_{t}^{i}}}_{2}^{2}}} \right)}}}}},} & (12)\end{matrix}$

where the function Γ(⋅, C_(t)) transforms the global rotation {hacekover (γ)}_(t) ^(i) or translation {hacek over (τ)}_(t) ^(i) to thecamera coordinates defined by C_(t), and w_(t) is a weighting factor forthe translation term.

The third term E_(reg) regularizes the egocentric trajectory {hacek over(Ψ)}^(i) to stay close to the output {circumflex over (Ψ)}^(i) of thetrajectory predictor:

$\begin{matrix}{{E_{reg} = {\frac{1}{NT}{\sum\limits_{i = 1}^{N}{\sum\limits_{t = 1}^{T}{{w_{\psi} \circ \left( {{\check{\psi}}_{t}^{i} - {\hat{\psi}}_{t}^{i}} \right)}}_{2}^{2}}}}},} & (13)\end{matrix}$

where ∘ denotes the element-wise product and w_(ψ) is a weighting vectorfor each element inside the egocentric trajectory. As an exception, eachperson's initial xy position and heading (δ{hacek over (x)}₀ ^(i),δ{hacek over (y)}₀ ^(i), δ{hacek over (ϕ)}₀ ^(i)) ⊂{hacek over (ψ)}₀^(i) are not regularized as they need to be inferred from the video.

The fourth term E_(cam) measures the smoothness of the camera parametersC and the uprightness of the camera:

$\begin{matrix}{{E_{cam} = {{\frac{1}{T}{\sum\limits_{t = 1}^{T}\left\langle {C_{t}^{y},Y} \right\rangle}} + {\frac{1}{T - 1}{\sum\limits_{t = 1}^{T - 1}{{C_{t + 1}^{\gamma} \ominus C_{t}^{\gamma}}}_{a}^{2}}} + {{C_{t + 1}^{\tau} - C_{t}^{\tau}}}_{2}^{2}}},} & (14)\end{matrix}$

Where

⋅, ⋅

denotes the inner product, C_(t) ^(y) is the +y vector of the cameraC_(t), and Y is the global up direction. C_(t) ^(γ) and C_(t) ^(τ)denote the rotation and translation of the camera C_(t).

The final term E_(pen) is a signed distance field (SDF)-basedinter-person penetration loss.

An approach is provided to recover 3D human meshes in consistent globalcoordinates from videos captured by a dynamic camera. To achieve this, anovel Transformer-based generative motion infiller addresses severeocclusions that often come with dynamic cameras. To resolve ambiguity inthe joint reconstruction of global human motions and camera poses,global human trajectories are predicted from their local body motions.Finally, a global optimization framework refines the predictedtrajectories and uses them as anchors for camera optimization.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A method comprising, at a device: performingmotion infilling for one or more tracked objects within a video;predicting a global trajectory for each of the one or more trackedobjects within the video; and for the one or more tracked objects withinthe video, combining the infilled motion for the tracked object with theglobal trajectory for the tracked object to determine infilled globalmotion for the tracked object.
 2. The method of claim 1, wherein thevideo is monocular.
 3. The method of claim 1, wherein the one or moretracked objects each have one or more missing portions resulting fromocclusion of the tracked objects in one or more frames of the video. 4.The method of claim 1, wherein the one or more tracked objects each haveone or more missing portions resulting from truncation of the trackedobjects in one or more frames of the video.
 5. The method of claim 1,wherein the one or more tracked objects each have one or more missingportions resulting from the object moving out of a scene in one or moreframes of the video.
 6. The method of claim 1, wherein in response toidentifying one or more missing portions for one of the tracked objectswithin a predetermined frame of the video, previous pose and shape datafor the tracked object is identified within previous frames of thevideo.
 7. The method of claim 6, wherein the previous pose and shapedata for the tracked object is input into a trained neural networkarchitecture, where the trained neural network architecture predictspose and shape data for the object within the predetermined frame of thevideo, and uses the predicted pose and shape data to fill in the missingportions for the tracked object within the predetermined frame of thevideo.
 8. The method of claim 1, wherein a translation and globalorientation is removed from the one or more tracked objects within thevideo prior to performing the motion infilling.
 9. The method of claim8, wherein after the motion infilling is performed for each of the oneor more tracked objects within the video, the global trajectory ispredicted for each of the one or more tracked object, using infilledlocal motion for each of the one or more tracked objects.
 10. The methodof claim 1, wherein the infilled global motion is determined withrespect to a global coordinate system.
 11. The method of claim 1,wherein one or more camera parameters are accounted for during thedetermination of the infilled global motion.
 12. A system comprising: ahardware processor of a device that is configured to: perform motioninfilling for one or more tracked objects within a video; predict aglobal trajectory for each of the one or more tracked objects within thevideo; and for the one or more tracked objects within the video, combinethe infilled motion for the tracked object with the global trajectoryfor the tracked object to determine infilled global motion for thetracked object.
 13. The system of claim 12, wherein the video ismonocular.
 14. The system of claim 12, wherein the one or more trackedobjects each have one or more missing portions resulting from occlusionof the tracked objects in one or more frames of the video.
 15. Thesystem of claim 12, wherein the one or more tracked objects each haveone or more missing portions resulting from truncation of the trackedobjects in one or more frames of the video.
 16. The system of claim 12,wherein the one or more tracked objects each have one or more missingportions resulting from the object moving out of a scene in one or moreframes of the video.
 17. The system of claim 12, wherein in response toidentifying one or more missing portions for one of the tracked objectswithin a predetermined frame of the video, previous pose and shape datafor the tracked object is identified within previous frames of thevideo.
 18. The system of claim 17, wherein the previous pose and shapedata for the tracked object is input into a trained neural networkarchitecture, where the trained neural network architecture predictspose and shape data for the object within the predetermined frame of thevideo, and uses the predicted pose and shape data to fill in the missingportions for the tracked object within the predetermined frame of thevideo.
 19. A non-transitory computer-readable storage medium storinginstructions that, when executed by a processor of a device, causes theprocessor to cause the device to: perform motion infilling for one ormore tracked objects within a video; predict a global trajectory foreach of the one or more tracked objects within the video; and for theone or more tracked objects within the video, combine the infilledmotion for the tracked object with the global trajectory for the trackedobject to determine infilled global motion for the tracked object. 20.The computer-readable storage medium of claim 19, wherein: in responseto identifying one or more missing portions for one of the trackedobjects within a predetermined frame of the video, previous pose andshape data for the tracked object is identified within previous framesof the video, and the previous pose and shape data for the trackedobject is input into a trained neural network architecture, where thetrained neural network architecture predicts pose and shape data for theobject within the predetermined frame of the video, and uses thepredicted pose and shape data to fill in the missing portions for thetracked object within the predetermined frame of the video.